What is your experience with data quality and how can you improve it?

Think Data is like car, then what quality you will expect from the car company ? The era where we are living is data is more costly than anything. Every person is digitally data point now. His every activity is generating data, stored by someone refurbished by someone and utilize by someone. Domain itself can define it's own quality measures. We are living with IOT devices and Data is now Real time and Near real time. Tons of data that you need to process and analyze so Quality is another aspects that can measure in it's own cage. In my experience I would prefer consistency that refers the data being uniform, accurate and coherent across various database.

Data quality is defined as the degree to which data meets a company's expectations of accuracy, validity, completeness, and consistency. It is a critical aspect of data management, ensuring that the data used for analysis, reporting, and decision-making is reliable and trustworthy. Measuring data quality levels can help organizations identify data errors that need to be resolved and assess whether the data in their IT systems is fit to serve its intended purpose.

Data refers to raw facts, figures, and statistics collected or generated in the course of business operations. Data quality refers to the accuracy, completeness, reliability, and relevance of data for its intended purpose. Key Dimensions of Data Quality: Accuracy: Data reflects the real-world values it is supposed to represent. Completeness: All required data is available and no vital elements are missing. Consistency: Data is uniform and coherent across different databases and systems. Relevance: Data is applicable and beneficial for the intended use.

Data quality is nothing but a true representation of how robust any company's on-boarding and processes are. It's is this data that supports in outling user behaviour , user personal, customer experience and what more we need to do for out reach.

In 1991, Massachusetts Institute of Technology’s Richard Wang and Lisa Guarascio came up with 20 dimensions for Data Quality but IMHO, Data Quality comes down to 3 questions you should be able to answer. Is your data: 1) Complete, Fresh, Rich & Secure? 2) Trustworthy and Relevant? 3) Understandable, Actionable and Timely?

Quality define precision, Every business want the more optimal ways to get precise decision that statistically correct based on the data.

Without quality, Data is dead. Anyone who has struggled with either entering data in the right way or making sense of reports knows Data Quality is paramount. Any investment in a system, process or other data management vehicle will quickly deteriorate without data quality under control.

Improving data quality is vital for business success. Follow these steps: 1. Define clear data quality standards (accuracy, completeness, consistency, timeliness). 2. Implement data validation (profiling, cleansing, integrity checks). 3. Enhance data integration (consolidation, mapping, transformation) to ensure consistency. 4. Audit and verify data accuracy against reliable sources. 5. Establish data governance (roles, responsibilities, policies). 6. Educate employees on data quality best practices. 7. Monitor data quality metrics using tools, audits, alerts. 8. Encourage collaboration and communication across departments. 9. Regularly update and cleanse data, removing outdated records and correcting errors.

Data quality matters because it directly influences the accuracy of insights, informs strategic decision-making, enhances customer trust through reliable information, and ensures efficient business operations by providing a solid foundation for successful analytics and sales efforts. Data quality is crucial for compliance with regulations, ensuring that organizations adhere to legal standards and avoid penalties. Organizations with high data quality gain a competitive edge by making more accurate predictions, staying agile, and adapting quickly to market changes. Quality data reduces the risk of making decisions based on inaccurate information, contributing to a more secure and resilient business environment.

Customer contactability , best customer service and most importantly seamless user experience basis on user behavior can be outlined from accurate data. Business retention and revenue generation can be done post analysing customer appetite and customer behavior using data analytics

I have seen most of the data practitioners focused on curing the symptoms and not the root cause. Data are generated by applications: web, mobiles, internal applications used by distributors in telecommunication, by relationship managers in banking and consumers. These applications are often linked to complex processes, which require lot of information keyed by the employees and the consumers. Because entering all this information is not practical, we (as employees & consumers) end up entering fake information in order to complete the task faster (purchase of an item, customer onboarding etc…). As a result, we collect “bad data”, which can’t be used. I believe we need to address the clunky processes & applications: this is harder to do.

Regularly conduct data audits to identify and rectify inaccuracies, ensuring a clean and accurate dataset. Implement data governance policies and procedures to establish clear guidelines for data management and maintenance. Provide training for team members on the importance of data quality, emphasizing accurate data entry and maintenance practices. Utilize advanced data quality tools and technologies, such as CRM systems and specialized software, for automated checks and validations. Foster a culture of continuous improvement, encouraging regular feedback loops and addressing evolving data quality challenges promptly. Leverage external data sources for enrichment, ensuring comprehensive and informed decision-making.

The way I experience is to start with synthetic data, that can simulate the scenario . before executing on The production data. What ever decision you take to produce the better output with synthetic data. The same decision You can take with production and follow the same rules. So that your data will produce the same output. You can think about to create a process that can be automated and check your data consistency. Byte level of data matching is the another way for full proof the plan.

There are several ways to 'get there' but staying there requires cataloging/governance to provide the observability required that the data remains correct and consistent. Data and schemas are alway in flux; you need ways to understand what is changing and what impact will result.

Market is very unpredictable and so the data is. The way business is treating the data with their domain understanding what I feel. - Educate them about data - make it data centric - data availability across business.

1. Regularly audit and cleanse data to ensure accuracy and completeness. 2. Establish clear data governance policies for consistent quality standards. 3. Implement automated tools for continuous monitoring and validation. 4. Train and educate teams on the importance of data quality practices. 5. Integrate data quality checks seamlessly into the organizational workflow. 6. Enforce standardized data entry protocols to minimize errors. 7. Utilize advanced technologies like AI for real-time data quality assessments. 8. Foster a culture of accountability and shared responsibility for data quality. 9. Invest in reliable data sources to enhance overall data reliability. 10. Regularly update and enrich data using external sources for relevance.

I will start from the domain understanding that will Help me to understand the business. So that I can help the businesses to build the data model along with updated architecture. Data should be more generalize across the domain and devices. For me the - security - availability - quality would be three factors where I can showcase. Everything is built on top of that.

1 Understanding the data 2 Divide data into two parts sample data and Test data in a ratio of 20/80. 3 Apply Analytics on randam sample data 4 Conclude the result

Here are the top methods to improve data quality skills and experience. 1. Implement Rigorous Data Validation Procedures. 2. Utilize Data Cleansing Techniques. 3. Establish Data Governance Protocols. 4. Showcase Data Enrichment Initiatives. 5. Utilize Advanced Analytics for Quality Insights. 6. Lead Cross-Functional Data Collaboration. 7. Integrate Real-Time Data Monitoring. 8. Present Successful Case Studies. 9. Incorporate Data Quality Metrics. 10. Demonstrate Training and Awareness Programs. 11. Utilize Automation for Data Checks. 12. Engage in Continuous Learning

Poor Data Quality doesn't just impact your data or analytics. It also impacts every application built on top of your data architecture. Generative AI, for instance, will expose poor data in a massive way.

While poor data quality leads to bad analysis, waiting to get 100% clean data is not an option. Data analytics is knowing what data or how much data is “good enough” to deliver quality inputs to influence good business decisions. Knowing what problems you are solving should dictate what “good data” is. And ensure that level is not compromised.

One of the biggest issues with data quality is that the people who have the problem are not the people who can fix it, and the people who could fix it have no direct stake in the outcome. You don't need to assume malice or laziness: all else being equal, people will do the right thing most of the time. The problem is that other activities they have more direct ownership in will take priority, so ensuring data quality gets pushed down the to-do list — or even entirely off it. The solution is the same as for all these issues at the intersection of tech and culture: make it easy to do the right thing. Make data quality a built-in aspect of data capture, not a laborious afterthought.

Regulatory impact. Some business are heavily regulated and may require an ability to reconstruct past models and results. Poor data quality makes that challenge even harder, and potentially, regulatory fines higher.